scholarly journals Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

10.2196/18350 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18350 ◽  
Author(s):  
Tareq Nasralah ◽  
Omar El-Gayar ◽  
Yong Wang
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Data Quality ◽  
Prescription Drugs ◽  
Case Analysis ◽  
Media Content ◽  
Opioid Epidemic ◽  
Social Media Data ◽  
Social Media Text ◽  
Media Data

Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.

scholarly journals Social Media Text Mining Framework for Drug Abuse: An Opioid Crisis Case Analysis (Preprint)

2020 ◽  
Author(s):  
Tareq Nasralah ◽  
Omar El-Gayar ◽  
Yong Wang
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Data Quality ◽  
Prescription Drugs ◽  
Case Analysis ◽  
Media Content ◽  
Opioid Epidemic ◽  
Social Media Data ◽  
Abuse Case ◽  
Media Data

BACKGROUND Social media is considered a promising and viable source of data for gaining insights into various disease conditions, patients’ attitudes, behaviors, and medications. It can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate challenges and limitations surrounding the use such data. OBJECTIVE The objective of this research was to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. METHODS The proposed framework starts with defining different terms related to keywords, categories, and characteristics of the topic of interest. Next, we used Crimson Hexagon to collect data based on a search query that is informed by a drug abuse ontology developed using the identified terms. Then, we preprocessed the data and examined it’s quality using an evaluation matrix. Finally, suitable data analysis approach could be used to analyze the collected data. RESULTS The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media that relate to opioids. Results from the case analysis showed that the framework could improve the discovery and identification of topics on social media domains characterized by a plethora of highly diverse terms and a lack of commonly available dictionary/language by the community such as in the opioid and drug abuse case. CONCLUSIONS The proposed framework addressed challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. CLINICALTRIAL

Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective

2021 ◽  
pp. 227797522110118
Author(s):  
Amit K. Srivastava ◽  
Rajhans Mishra
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Quality Of Data ◽  
Social Media Data ◽  
National Crisis ◽  
Social Media Platforms ◽  
Quality Issues ◽  
The One ◽  
Media Data

Social media platforms have become very popular these days among individuals and organizations. On the one hand, organizations use social media as a potential tool to create awareness of their products among consumers, and on the other hand, social media data is useful to predict the national crisis, election polls, stock prediction, etc. However, nowadays, a debate is going on about the quality of data generated on social media platforms, whether it is relevant for prediction and generalization. The article discusses the relevance and quality of data obtained from social media in the context of research and development. Social media data quality issues may impact the generalizability and reproducibility of the results of the study. The paper explores possible reasons for quality issues in the data generated over social media platforms along with the suggestive measures to minimize them using the proposed social media data quality framework.

Images of protest in social media: Struggle over visibility and visual narratives

2018 ◽  
Vol 20 (11) ◽  
pp. 4293-4310 ◽  
Author(s):  
Christina Neumayer ◽  
Luca Rossi
Keyword(s):  
Social Media ◽  
Conceptual Understanding ◽  
Media Content ◽  
Political Protest ◽  
Frankfurt Am Main ◽  
Social Media Data ◽  
Visual Narratives ◽  
Empirical Inquiry ◽  
Visual Dimension ◽  
Media Data

While political protest is essentially a visual expression of dissent, both social movement research and media studies have thus far been hesitant to focus on visual social media data from protest events. This research explores the visual dimension (photos and videos) of Twitter communication in the Blockupy protests against the opening of the European Central Bank (ECB) headquarters in Frankfurt am Main on 18 March 2015. It does so through a novel combination of quantitative analysis, content analysis of images, and identification of narratives. The article concludes by arguing that the visual in political protest in social media reproduces existing visualities and hierarchies rather than challenges them. This research enhances our conceptual understanding of how activists’ struggles play out in the visual and contributes to developing methods for empirical inquiry into visual social media content.

scholarly journals A Multivariate Approach to Facebook Data for Marketing Communication

2016 ◽  
Author(s):  
Caterina Liberati ◽  
Elisa Arrigo ◽  
Paolo Mariani
Keyword(s):  
Social Media ◽  
Prescription Drugs ◽  
Multivariate Statistical ◽  
Corporate Brand ◽  
Social Media Data ◽  
Multivariate Statistical Approach ◽  
Marketing Activities ◽  
Legal Constraints ◽  
Communication Decisions ◽  
Media Data

The aim of this paper is to propose a method to explore and synthesize social media data in order to aid businesses to make their communication decisions. The research was conducted at the end of 2014 on 5607 Italian Facebook subjects interested in drugs and health. In this study, we refer to the pharmaceutical market that is characterized by strict legal constraints, which prevent any promotional activities (such as advertising) of companies on prescription drugs. Thus, pharmaceutical businesses tend to promote their corporate brand instead of a single product brand. In such context, social media offer the opportunity to gather customers’ information about their attitudes and preferences, helpful to address marketing activities. Through a multivariate statistical approach on Facebook data, we have highlighted the associations existing between TV channels and users’ profiles. Therefore, depending on the value proposition to promote, every business could choose, first, the target group to reach and, then, the nearest suitable channel where to develop the corporate brand communication.

scholarly journals Ranking Significant Features for Increasing Engagement on Social Media via Regression Analysis

2021 ◽  
Author(s):  
Thiago R. C. de Lima
Keyword(s):  
Social Media ◽  
Regression Models ◽  
Model Fit ◽  
Short Paper ◽  
Media Content ◽  
Linear Regression Models ◽  
Data Set ◽  
Social Media Data ◽  
Unseen Data ◽  
Media Data

Social media comprises of platforms that surpassed their initial goal to connect people just for the sake of socializing and currently provide powerful tools for businesses to reach millions of views worldwide, increasing their chances of gaining new customers. This short paper utilizes the Buzz in Social Media data set available at UCI Machine Learning Repository for identifying the attributes in social media content that have the highest correlation to the amount of repercussion it gained. To achieve such result, several linear regression models are constructed, then ranked based on their respective model fit measure (R-squared) and accuracy when tested against unseen data.

Daily and hourly mood pattern discovery of Turkish twitter users

2015 ◽  
Vol 5 (2) ◽  
pp. 90
Author(s):  
Mete Celik ◽  
Ahmet Sakir Dokuz
Keyword(s):  
Social Media ◽  
Big Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Social Media Data ◽  
Web Technologies ◽  
Document Frequency ◽  
Social Media Text ◽  
Twitter Users ◽  
Media Data

<p>Massive amount of data-related applications and widespread usage of web technologies has started big data era. Social media data is one of the big data sources. Mining social media data provides useful insights for companies and organizations for developing their services, products or organizations. This study aims to analyze Turkish Twitter users based on daily and hourly social media sharings. By this way, daily and hourly mood patterns of Turkish social media users could be revealed in positive or negative manner. For this purpose, Support Vector Machines (SVM) classification algorithm and Term Frequency – Inverse Document Frequency (TF-IDF) feature selection technique was used. As far as our knowledge, this is the first attempt to analyze people’s all sharings on social media and generate results for temporal-based indicators like macro and micro levels.</p><p> </p><p>Keywords: big data, social media, text classification, svm, tf-idf term weighting, daily and hourly mood patterns.</p>

scholarly journals SEARCHING FOR THE FORGOTTEN: EXAMINING THE ONLINE PRESENCE OF VICTIMS OF POLICE KILLINGS

2021 ◽  
Author(s):  
Jennifer Pierre ◽  
Morgan Currie ◽  
Britt Paris ◽  
Irene Pasquetto
Keyword(s):  
Social Media ◽  
Potential Role ◽  
Media Content ◽  
Social Media Data ◽  
Social Media Mining ◽  
Source Of Information ◽  
Media Data ◽  
Official Police ◽  
Media Mining

This paper examines the potential role of social media in enhancing the understanding and perception of victims of police killings and the data collection surrounding these incidents. Through a series of content analysis and social media mining exercises, the authors observe the emergence of three distinct types of social media content offered on victims of police killings: persistence of the deceased’s activity across social media, sensational commentary on videos and blog postings, and memorials on Facebook, Twitter, and Tumblr. As part of a larger investigation of the availability and accessibility of official police homicide data, this paper aims to present social media data as a potentially powerful source of information to supplement quantitative reports. This process may be especially useful for the most affected communities, particularly BIPOC communities.

scholarly journals Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

2021 ◽  
Vol 10 (7) ◽  
pp. 425
Author(s):  
Nan Cui ◽  
Nick Malleson ◽  
Victoria Houlden ◽  
Alexis Comber
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Rural Areas ◽  
Green Space ◽  
Urban Green Space ◽  
Geographical Information ◽  
Future Research ◽  
Social Media Data ◽  
Urban Green ◽  
Media Data

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis.

scholarly journals Near real time flood inundation mapping using social media data as an information source: a case study of 2015 Chennai flood

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dhivya Karmegam ◽  
Sivakumar Ramamoorthy ◽  
Bagavandas Mappillairaju
Keyword(s):  
Social Media ◽  
Real Time ◽  
Water Depth ◽  
Information Source ◽  
Media Content ◽  
Social Media Data ◽  
Inundation Mapping ◽  
Chennai City ◽  
Media Data ◽  
Water Height

AbstractDuring and just after flash flood, data regarding water extent and inundation will not be available as the traditional data collection methods fail during disasters. Rapid water extent map is vital for disaster responders to identify the areas of immediate need. Real time data available in social networking sites like Twitter and Facebook is a valuable source of information for response and recovery, if handled in an efficient way. This study proposes a method for mining social media content for generating water inundation mapping at the time of flood. The case of 2015 Chennai flood was considered as the disaster event and 95 water height points with geographical coordinates were derived from social media content posted during the flood. 72 points were within Chennai and based on these points water extent map was generated for the Chennai city by interpolation. The water depth map generated from social media information was validated using the field data. The root mean square error between the actual water height data and extracted social media data was ± 0.3 m. The challenge in using social media data is to filter the messages that have water depth related information from the ample amount of messages posted in social media during disasters. Keyword based query was developed and framed in MySQL to filter messages that have location and water height mentions. The query was validated with tweets collected during the floods that hit Mumbai city in July 2019. The validation results confirm that the query reduces the volume of tweets for manual evaluation and in future will aid in mapping the water extent in near real time at the time of floods.

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